class_docstr: Run data (metrics and parameters).
class_docstr_tok: ["Run","data","(","metrics","and","parameters",")","."]
docstr: Construct a new mlflow.entities.RunData instance.
  
  Args:
      metrics: List of mlflow.entities.Metric.
      params: List of mlflow.entities.Param.
      tags: List of mlflow.entities.RunTag.
docstr_tok: ["Construct","a","new","mlflow.entities.RunData","instance",".","Args",":","metrics",":","List","of","mlflow.entities.Metric",".","params",":","List","of","mlflow.entities.Param",".","tags",":","List","of","mlflow.entities.RunTag","."]
code: def __init__(self, metrics=None, params=None, tags=None):
      """Construct a new mlflow.entities.RunData instance.
  
      Args:
          metrics: List of mlflow.entities.Metric.
          params: List of mlflow.entities.Param.
          tags: List of mlflow.entities.RunTag.
  
      """
      # Maintain the original list of metrics so that we can easily convert it back to
      # protobuf
      self._metric_objs = metrics or []
      self._metrics = {
          metric.key: metric.value for metric in self._metric_objs
      }
      self._params = {
          param.key: param.value for param in (params or [])
      }
      self._tags = {tag.key: tag.value for tag in (tags or [])}
  
file_path: mlflow/mlflow/entities/run_data.py
imports: from mlflow.entities._mlflow_object import _MLflowObject
  from mlflow.entities.metric import Metric
  from mlflow.entities.param import Param
  from mlflow.entities.run_tag import RunTag
  from mlflow.protos.service_pb2 import Param
  from mlflow.protos.service_pb2 import RunData
  from mlflow.protos.service_pb2 import RunTag
label_desc: Use image node_id 1 to create a new RunData object from inherited base classes: _MLflowObject with example: obj = RunData(metrics, params, tags)